Introduction: Coronavirus disease 2019 (COVID-19) is a serious illness that can affect multiple organs including the lungs. The COVID-mortality risk is attributed to the quick transmission of the virus, the severity of disease, and preclinical risk factors, such as the presence of comorbidities. High-resolution computed tomography (HRCT) can predict disease severity in COVID-19 patients.
Methodology: This was a retrospective cohort study in which data were obtained from COVID centers at tertiary care hospitals in Azad Jammu and Kashmir. Details of clinical characteristics and HRCT findings along with details of smoking and comorbid history were obtained.
Results: Fever at hospital admission, HRCT findings, and having a partner predicted disease severity showed a significant p-value of <0.05. Old age and living in a combined household were associated with severe outcomes (p<0.05). Symptoms of shortness of breath (SOB) on hospital admission could predict the need for ICU admission in COVID-19 patients.
Conclusion: HRCT has a good predictive value for disease severity in patients with COVID-19, and old age is a risk factor. Although, limited associations were established in the analysis, in this study hyperlipidemia and hypertension significantly affected the course of disease. Further studies should be done to explore the relationship.
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http://dx.doi.org/10.7759/cureus.52937 | DOI Listing |
Physiol Meas
January 2025
Universita Cattolica del Sacro Cuore, Rome, Italy, Largo Francesco Vito, 1, 00168 Roma RM, Italy, Rome, 00168, ITALY.
Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF.
View Article and Find Full Text PDFEur Radiol Exp
January 2025
Division of Cardiothoracic Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA.
Background: This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).
Methods: Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions.
J Family Med Prim Care
December 2024
Vice Chancellor, Atal Bihari Vajpayee Medical University, Lucknow, Uttar Pradesh, India.
Background: It is documented that COVID-19 survivors have prolonged morbidity and functional impairment for many years. Data regarding post-COVID-19 lung functions is lacking from the Indian population. We aim to evaluate the lung functions in such patients after 3-6 months of hospital discharge.
View Article and Find Full Text PDFBMC Pulm Med
January 2025
Tehran Lung Research and Developmental Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: This study aims to compare Lung Ultrasound (LUS) findings with High-Resolution Computerized Tomography (HRCT) and Pulmonary Function Tests (PFTs) to detect the severity of lung involvement in patients with Usual Interstitial Pneumonia (UIP) and Non-Specific Interstitial Pneumonia (NSIP).
Methods: A cross-sectional study was conducted on 35 UIP and 30 NSIP patients at a referral hospital. All patients underwent LUS, HRCT, and PFT.
Diagnostics (Basel)
December 2024
Department of Respiratory Medicine, JSS Medical College, JSS Academy of Higher Education & Research (JSS AHER), Mysore 570004, Karnataka, India.
Thin-section CT (TSCT) is currently the most sensitive imaging modality for detecting bronchiectasis. However, conventional TSCT or HRCT may overlook subtle lung involvement such as alveolar and interstitial changes. Artificial Intelligence (AI)-based analysis offers the potential to identify novel information on lung parenchymal involvement that is not easily detectable with traditional imaging techniques.
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